Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	145,006
2	Rhode Island	143,771
3	South Dakota	140,593
4	Utah	127,897
5	Tennessee	124,853
6	Arizona	121,940
7	Iowa	118,143
8	Wisconsin	116,208
9	Nebraska	115,739
10	South Carolina	115,618
11	Oklahoma	114,969
12	New Jersey	114,841
13	Arkansas	114,112
14	Delaware	112,369
15	Indiana	111,876
16	Alabama	111,797
17	Illinois	109,850
18	Kansas	109,031
19	Idaho	108,507
20	New York	108,373
21	Mississippi	107,329
22	Florida	107,124
23	Minnesota	107,109
24	Nevada	106,433
25	Wyoming	105,921
26	Montana	105,781
27	Georgia	104,050
28	Kentucky	103,800
29	Massachusetts	102,867
30	Texas	102,547
31	Louisiana	102,492
32	Missouri	101,479
33	Michigan	99,736
34	Connecticut	97,753
35	New Mexico	97,476
36	Colorado	96,308
37	North Carolina	96,295
38	California	96,283
39	Alaska	95,868
40	Pennsylvania	94,811
41	Ohio	94,748
42	West Virginia	91,075
43	Virginia	79,426
44	Maryland	76,353
45	New Hampshire	72,940
46	District of Columbia	69,731
47	Washington	58,735
48	Puerto Rico	54,189
49	Maine	51,057
50	Oregon	48,713
51	Vermont	39,005
52	Hawaii	25,286

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Wyoming	108
2	Louisiana	79
3	Nevada	74
4	Missouri	73
5	Utah	62
6	Colorado	60
7	Idaho	59
8	Montana	58
9	Alabama	57
10	New Mexico	55
11	Washington	55
12	Arkansas	54
13	Arizona	53
14	West Virginia	52
15	North Carolina	46
16	Oklahoma	45
17	Oregon	44
18	Indiana	36
19	Mississippi	35
20	Delaware	33
21	Kentucky	33
22	South Carolina	31
23	Alaska	30
24	Kansas	29
25	Rhode Island	29
26	Michigan	27
27	Texas	27
28	New Hampshire	26
29	District of Columbia	25
30	Hawaii	25
31	New Jersey	25
32	Pennsylvania	24
33	Iowa	23
34	Tennessee	23
35	California	22
36	Georgia	22
37	Maine	22
38	Minnesota	20
39	North Dakota	20
40	Ohio	20
41	New York	18
42	Wisconsin	18
43	Illinois	16
44	Nebraska	16
45	Connecticut	15
46	Puerto Rico	13
47	South Dakota	12
48	Maryland	11
49	Vermont	10
50	Virginia	10
51	Massachusetts	8
52	Florida	0

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,966
2	New York	2,724
3	Massachusetts	2,604
4	Rhode Island	2,570
5	Mississippi	2,473
6	Arizona	2,442
7	Connecticut	2,317
8	Alabama	2,300
9	Louisiana	2,293
10	South Dakota	2,290
11	Pennsylvania	2,153
12	Michigan	2,083
13	New Mexico	2,052
14	Indiana	2,042
15	North Dakota	2,036
16	Illinois	2,012
17	Arkansas	1,944
18	Iowa	1,934
19	Georgia	1,930
20	South Carolina	1,901
21	Oklahoma	1,851
22	Nevada	1,828
23	Tennessee	1,816
24	Texas	1,798
25	Kansas	1,770
26	Florida	1,735
27	Delaware	1,724
28	Ohio	1,721
29	Kentucky	1,615
30	District of Columbia	1,612
31	Maryland	1,602
32	Missouri	1,600
33	California	1,599
34	West Virginia	1,593
35	Montana	1,541
36	Wisconsin	1,377
37	Minnesota	1,349
38	Virginia	1,327
39	Nebraska	1,301
40	North Carolina	1,269
41	Wyoming	1,268
42	Colorado	1,187
43	Idaho	1,185
44	New Hampshire	1,003
45	Puerto Rico	794
46	Washington	767
47	Utah	724
48	Oregon	655
49	Maine	630
50	Alaska	486
51	Vermont	410
52	Hawaii	355

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Wyoming	5
2	Kansas	2
3	Alabama	1
4	Kentucky	1
5	Michigan	1
6	Missouri	1
7	Montana	1
8	Pennsylvania	1
9	Virginia	1
10	West Virginia	1
11	Alaska	0
12	Arizona	0
13	Arkansas	0
14	California	0
15	Colorado	0
16	Connecticut	0
17	Delaware	0
18	District of Columbia	0
19	Florida	0
20	Georgia	0
21	Hawaii	0
22	Idaho	0
23	Illinois	0
24	Indiana	0
25	Iowa	0
26	Louisiana	0
27	Maine	0
28	Maryland	0
29	Massachusetts	0
30	Minnesota	0
31	Mississippi	0
32	Nebraska	0
33	Nevada	0
34	New Hampshire	0
35	New Jersey	0
36	New Mexico	0
37	New York	0
38	North Carolina	0
39	North Dakota	0
40	Ohio	0
41	Oklahoma	0
42	Oregon	0
43	Puerto Rico	0
44	Rhode Island	0
45	South Carolina	0
46	South Dakota	0
47	Tennessee	0
48	Texas	0
49	Utah	0
50	Vermont	0
51	Washington	0
52	Wisconsin	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Chattahoochee	Georgia	404,419	1	99
Crowley	Colorado	364,626	2	99
Bent	Colorado	276,851	3	99
Dewey	South Dakota	250,849	4	99
Lincoln	Arkansas	247,543	5	99
Davidson	Tennessee	143,338	195	93
Richland	South Carolina	113,943	999	68
York	South Carolina	113,795	1005	68
Orange	California	85,898	2270	27
Pierce	Washington	62,254	2830	9

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Foard	Texas	8,658	1	99
Galax city	Virginia	8,350	2	99
Hancock	Georgia	8,159	3	99
Jerauld	South Dakota	7,948	4	99
Emporia city	Virginia	7,856	5	99
Orange	California	1,607	1857	40
York	South Carolina	1,392	2110	32
Richland	South Carolina	1,383	2123	32
Davidson	Tennessee	1,363	2148	31
Pierce	Washington	697	2804	10

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.